705 citations · 1.4k across the 22 of their papers we have counts for
23 papers
General Fourier Feature Physics-Informed Extreme Learning Machine (GFF-PIELM) for High-Frequency PDEs
Fei Ren, Sifan Wang, Pei-Zhi Zhuang +2
Conventional physics-informed extreme learning machine (PIELM) often faces challenges in solving partial differential equations (PDEs) involving high-frequency and variable-frequen…
A Practitioner's Guide to Kolmogorov-Arnold Networks
Amir Noorizadegan, Sifan Wang, Leevan Ling +1
Kolmogorov-Arnold Networks (KANs), whose design is inspired-rather than dictated-by the Kolmogorov superposition theorem, have emerged as a structured alternative to MLPs. This rev…
Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
Sifan Wang, Shyam Sankaran, Xiantao Fan +2
Turbulent fluid flows are among the most computationally demanding problems in science, requiring enormous computational resources that become prohibitive at high flow speeds. Phys…
Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
Sifan Wang, Ananyae Kumar Bhartari, Bowen Li +1
Multi-task learning through composite loss functions is fundamental to modern deep learning, yet optimizing competing objectives remains challenging. We present new theoretical and…
Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems
Shunyuan Mao, Ruobing Dong, Kwang Moo Yi +3
We introduce Disk2Planet, a machine learning-based tool to infer key parameters in disk-planet systems from observed protoplanetary disk structures. Disk2Planet takes as input the…
Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials
Sifan Wang, Tong-Rui Liu, Shyam Sankaran +1
Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods.…
CViT: Continuous Vision Transformer for Operator Learning
Sifan Wang, Jacob H Seidman, Shyam Sankaran +3
Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various…
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks
Sifan Wang, Bowen Li, Yuhan Chen +1
While physics-informed neural networks (PINNs) have become a popular deep learning framework for tackling forward and inverse problems governed by partial differential equations (P…
Learning Only On Boundaries: a Physics-Informed Neural operator for Solving Parametric Partial Differential Equations in Complex Geometries
Zhiwei Fang, Sifan Wang, Paris Perdikaris
Recently deep learning surrogates and neural operators have shown promise in solving partial differential equations (PDEs). However, they often require a large amount of training d…
An Expert's Guide to Training Physics-informed Neural Networks
Sifan Wang, Shyam Sankaran, Hanwen Wang +1
Physics-informed neural networks (PINNs) have been popularized as a deep learning framework that can seamlessly synthesize observational data and partial differential equation (PDE…
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems
Shunyuan Mao, Ruobing Dong, Lu Lu +3
We develop a tool, which we name Protoplanetary Disk Operator Network (PPDONet), that can predict the solution of disk-planet interactions in protoplanetary disks in real-time. We…
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
Zhiwei Fang, Sifan Wang, Paris Perdikaris
While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date, PINNs have not been successful in simulating multi-scale and singular perturbatio…
Random Weight Factorization Improves the Training of Continuous Neural Representations
Sifan Wang, Hanwen Wang, Jacob H. Seidman +1
Continuous neural representations have recently emerged as a powerful and flexible alternative to classical discretized representations of signals. However, training them to captur…
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling
Arka Daw, Jie Bu, Sifan Wang +2
Despite the success of physics-informed neural networks (PINNs) in approximating partial differential equations (PDEs), PINNs can sometimes fail to converge to the correct solution…
Respecting causality is all you need for training physics-informed neural networks
Sifan Wang, Shyam Sankaran, Paris Perdikaris
While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhi…
Fast PDE-constrained optimization via self-supervised operator learning
Sifan Wang, Mohamed Aziz Bhouri, Paris Perdikaris
Design and optimal control problems are among the fundamental, ubiquitous tasks we face in science and engineering. In both cases, we aim to represent and optimize an unknown (blac…
Improved architectures and training algorithms for deep operator networks
Sifan Wang, Hanwen Wang, Paris Perdikaris
Operator learning techniques have recently emerged as a powerful tool for learning maps between infinite-dimensional Banach spaces. Trained under appropriate constraints, they can…
Long-time integration of parametric evolution equations with physics-informed DeepONets
Sifan Wang, Paris Perdikaris
Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. I…
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets
Sifan Wang, Hanwen Wang, Paris Perdikaris
Deep operator networks (DeepONets) are receiving increased attention thanks to their demonstrated capability to approximate nonlinear operators between infinite-dimensional Banach…
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
Sifan Wang, Hanwen Wang, Paris Perdikaris
Physics-informed neural networks (PINNs) are demonstrating remarkable promise in integrating physical models with gappy and noisy observational data, but they still struggle in cas…
When and why PINNs fail to train: A neural tangent kernel perspective
Sifan Wang, Xinling Yu, Paris Perdikaris
Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial…
Deep learning of free boundary and Stefan problems
Sifan Wang, Paris Perdikaris
Free boundary problems appear naturally in numerous areas of mathematics, science and engineering. These problems present a great computational challenge because they necessitate n…
Understanding and mitigating gradient pathologies in physics-informed neural networks
Sifan Wang, Yujun Teng, Paris Perdikaris
The widespread use of neural networks across different scientific domains often involves constraining them to satisfy certain symmetries, conservation laws, or other domain knowled…